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  • databases  (15)
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  • 1
    Language: English
    In: Neural Networks, October 2012, Vol.34, pp.80-95
    Description: In this paper, we propose a novel framework based on a collective network of evolutionary binary classifiers (CNBC) to address the problems of feature and class scalability. The main goal of the proposed framework is to achieve a high classification performance over dynamic audio and video repositories. The proposed framework adopts a “Divide and Conquer” approach in which an individual network of binary classifiers (NBC) is allocated to discriminate each audio class. An search is applied to find the best binary classifier in each NBC with respect to a given criterion. Through the incremental evolution sessions, the CNBC framework can dynamically adapt to each new incoming class or feature set without resorting to a full-scale re-training or re-configuration. Therefore, the CNBC framework is particularly designed for dynamically varying databases where no conventional static classifiers can adapt to such changes. In short, it is entirely a novel topology, an unprecedented approach for dynamic, content/data adaptive and scalable audio classification. A large set of audio features can be effectively used in the framework, where the CNBCs make appropriate selections and combinations so as to achieve the highest discrimination among individual audio classes. Experiments demonstrate a high classification accuracy (above 90%) and efficiency of the proposed framework over large and dynamic audio databases.
    Keywords: Audio Content-Based Classification ; Evolutionary Neural Networks ; Particle Swarm Optimization ; Multilayer Perceptron ; Computer Science
    ISSN: 0893-6080
    E-ISSN: 1879-2782
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  • 2
    Language: English
    In: Expert Systems With Applications, 2011, Vol.38(4), pp.3220-3226
    Description: This paper presents a personalized long-term electrocardiogram (ECG) classification framework, which addresses the problem within a long-term ECG signal, known as register, recorded from an individual patient. Due to the massive amount of ECG beats in a register, visual inspection is quite difficult and cumbersome, if not impossible. Therefore, the proposed system helps professionals to quickly and accurately diagnose any latent heart disease by examining only the representative beats (the so-called master key-beats) each of which is automatically extracted from a time frame of homogeneous (similar) beats. We tested the system on a benchmark database where beats of each register have been manually labeled by cardiologists. The selection of the right master key-beats is the key factor for achieving a highly accurate classification and thus we used -means clustering in order to find out (near-) optimal number of key-beats as well as the master key-beats. The classification process produced results that were consistent with the manual labels with over 99% average accuracy, which basically shows the efficiency and the robustness of the proposed system over massive data (feature) collections in high dimensions.
    Keywords: Personalized Long-Term ECG Classification ; Exhaustive K-Means Clustering ; Holter Registers ; Computer Science
    ISSN: 0957-4174
    E-ISSN: 1873-6793
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  • 3
    Language: English
    In: IEEE Transactions on Biomedical Engineering, March 2016, Vol.63(3), pp.664-675
    Description: Goal: This paper presents a fast and accurate patient-specific electrocardiogram (ECG) classification and monitoring system. Methods: An adaptive implementation of 1-D convolutional neural networks (CNNs) is inherently used to fuse the two major blocks of the ECG classification into a single learning body: feature extraction and classification. Therefore, for each patient, an individual and simple CNN will be trained by using relatively small common and patient-specific training data, and thus, such patient-specific feature extraction ability can further improve the classification performance. Since this also negates the necessity to extract hand-crafted manual features, once a dedicated CNN is trained for a particular patient, it can solely be used to classify possibly long ECG data stream in a fast and accurate manner or alternatively, such a solution can conveniently be used for real-time ECG monitoring and early alert system on a light-weight wearable device. Results: The results over the MIT-BIH arrhythmia benchmark database demonstrate that the proposed solution achieves a superior classification performance than most of the state-of-the-art methods for the detection of ventricular ectopic beats and supraventricular ectopic beats. Conclusion: Besides the speed and computational efficiency achieved, once a dedicated CNN is trained for an individual patient, it can solely be used to classify his/her long ECG records such as Holter registers in a fast and accurate manner. Significance: Due to its simple and parameter invariant nature, the proposed system is highly generic, and, thus, applicable to any ECG dataset.
    Keywords: Electrocardiography ; Neurons ; Feature Extraction ; Kernel ; Databases ; Training ; Monitoring ; Patient-Specific ECG Classification ; Convolutional Neural Networks ; Real-Time Heart Monitoring ; Medicine ; Engineering
    ISSN: 0018-9294
    E-ISSN: 1558-2531
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  • 4
    Language: English
    In: Journal of Biomedical Informatics, June 2014, Vol.49, pp.16-31
    Description: The illustration of the proposed EEG classification system (top). The illustration of the evolution process of a CNBC (bottom). This paper presents a novel systematic approach for patient-specific classification of long-term Electroencephalography (EEG). The goal is to extract the seizure sections with a high accuracy to ease the Neurologist’s burden of inspecting such long-term EEG data. We aim to achieve this using the minimum feedback from the Neurologist. To accomplish this, we use the majority of the state-of-the-art features proposed in this domain for evolving a collective network of binary classifiers (CNBC) using multi-dimensional particle swarm optimization (MD PSO). Multiple CNBCs are then used to form a CNBC ensemble (CNBC-E), which aggregates epileptic seizure frames from the classification map of each CNBC in order to maximize the sensitivity rate. Finally, a morphological filter forms the final epileptic segments while filtering out the outliers in the form of classification noise. The proposed system is fully generic, which does not require any information about the patient such as the list of relevant EEG channels. The results of the classification experiments, which are performed over the benchmark CHB-MIT scalp long-term EEG database show that the proposed system can achieve all the aforementioned objectives and exhibits a significantly superior performance compared to several other state-of-the-art methods. Using a limited training dataset that is formed by less than 2 min of seizure and 24 min of non-seizure data on the average taken from the early 25% section of the EEG record of each patient, the proposed system establishes an average sensitivity rate above 89% along with an average specificity rate above 93% over the test set.
    Keywords: EEG Classification ; Seizure Event Detection ; Evolutionary Classifiers ; Morphological Filtering ; Medicine ; Engineering ; Public Health
    ISSN: 1532-0464
    E-ISSN: 1532-0480
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  • 5
    Language: English
    In: IEEE Transactions on Neural Systems and Rehabilitation Engineering, March 2016, Vol.24(3), pp.386-398
    Description: In this paper, the performance of the phase space representation in interpreting the underlying dynamics of epileptic seizures is investigated and a novel patient-specific seizure detection approach is proposed based on the dynamics of EEG signals. To accomplish this, the trajectories of seizure and nonseizure segments are reconstructed in a high dimensional space using time-delay embedding method. Afterwards, Principal Component Analysis (PCA) was used in order to reduce the dimension of the reconstructed phase spaces. The geometry of the trajectories in the lower dimensions is then characterized using Poincaré section and seven features were extracted from the obtained intersection sequence. Once the features are formed, they are fed into a two-layer classification scheme, comprising the Linear Discriminant Analysis (LDA) and Naive Bayesian classifiers. The performance of the proposed method is then evaluated over the CHB-MIT benchmark database and the proposed approach achieved 88.27% sensitivity and 93.21% specificity on average with 25% training data. Finally, we perform comparative performance evaluations against the state-of-the-art methods in this domain which demonstrate the superiority of the proposed method.
    Keywords: Electroencephalography ; Feature Extraction ; Trajectory ; Nonlinear Dynamical Systems ; Epilepsy ; Geometry ; Benchmark Testing ; Dynamics ; Electroencephalography (EEG) ; Phase Space ; Poincaré Section ; Seizure Detection ; Two-Layer Classifier Topology ; Occupational Therapy & Rehabilitation
    ISSN: 1534-4320
    E-ISSN: 1558-0210
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  • 6
    Language: English
    In: 2014 22nd European Signal Processing Conference (EUSIPCO), September 2014, pp.785-789
    Description: Many modern computer vision systems combine high dimensional features and linear classifiers to achieve better classification accuracy. However, the excessively long features are often highly redundant; thus dramatically increases the system storage and computational load. This paper presents a novel feature selection algorithm, namely cardinal sparse partial least square algorithm, to address this deficiency in an effective way. The proposed algorithm is based on the sparse solution of partial least square regression. It aims to select a sufficiently large number of features, which can achieve good accuracy when used with linear classifiers. We applied the algorithm to a face recognition system and achieved the stateof- the-art results with significantly shorter feature vectors.
    Keywords: Face ; Face Recognition ; Computer Vision ; Databases ; Vectors ; Conferences ; Signal Processing Algorithms ; Feature Selection ; Sparse Partial Least Square ; Face Recognition ; Engineering
    ISSN: 2219-5491
    Source: IEEE Conference Publications
    Source: IEEE Xplore
    Source: IEEE Journals & Magazines 
    Source: IEEE eBooks
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  • 7
    Language: English
    In: 2014 5th European Workshop on Visual Information Processing (EUVIP), December 2014, pp.1-6
    Description: Several existing content-based image retrieval and classification systems rely on low-level features which are automatically extracted from images. However, often these features lack the discrimination power needed for accurate description of the image content and hence they may lead to a poor retrieval or classification performance. This article applies an evolutionary feature synthesis method based on multi-dimensional particle swarm optimization on low-level image features to enhance their discrimination ability. The proposed method can be applied on any database and low-level features as long as some ground-truth information is available. Content-based image retrieval experiments show that a significant performance improvement can be achieved.
    Keywords: Vectors ; Feature Extraction ; Synthesizers ; Particle Swarm Optimization ; Training ; Databases ; Transforms ; Content-Based Image Retrieval ; Evolutionary Feature Synthesis ; Multi-Dimensional Particle Swarm Optimization
    Source: IEEE Conference Publications
    Source: IEEE Xplore
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  • 8
    Language: English
    In: Conference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual Conference, 2015, Vol.2015, pp.2608-11
    Description: We propose a fast and accurate patient-specific electrocardiogram (ECG) classification and monitoring system using an adaptive implementation of 1D Convolutional Neural Networks (CNNs) that can fuse feature extraction and classification into a unified learner. In this way, a dedicated CNN will be trained for each patient by using relatively small common and patient-specific training data and thus it can also be used to classify long ECG records such as Holter registers in a fast and accurate manner. Alternatively, such a solution can conveniently be used for real-time ECG monitoring and early alert system on a light-weight wearable device. The experimental results demonstrate that the proposed system achieves a superior classification performance for the detection of ventricular ectopic beats (VEB) and supraventricular ectopic beats (SVEB).
    Keywords: Algorithms ; Neural Networks (Computer)
    ISBN: 9781424492718
    ISSN: 1557-170X
    ISSN: 1094687X
    E-ISSN: 15584615
    Source: MEDLINE/PubMed (U.S. National Library of Medicine)
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  • 9
    Language: English
    In: IEEE transactions on bio-medical engineering, May 2009, Vol.56(5), pp.1415-26
    Description: This paper presents a generic and patient-specific classification system designed for robust and accurate detection of ECG heartbeat patterns. The proposed feature extraction process utilizes morphological wavelet transform features, which are projected onto a lower dimensional feature space using principal component analysis, and temporal features from the ECG data. For the pattern recognition unit, feedforward and fully connected artificial neural networks, which are optimally designed for each patient by the proposed multidimensional particle swarm optimization technique, are employed. By using relatively small common and patient-specific training data, the proposed classification system can adapt to significant interpatient variations in ECG patterns by training the optimal network structure, and thus, achieves higher accuracy over larger datasets. The classification experiments over a benchmark database demonstrate that the proposed system achieves such average accuracies and sensitivities better than most of the current state-of-the-art algorithms for detection of ventricular ectopic beats (VEBs) and supra-VEBs (SVEBs). Over the entire database, the average accuracy-sensitivity performances of the proposed system for VEB and SVEB detections are 98.3%-84.6% and 97.4%-63.5%, respectively. Finally, due to its parameter-invariant nature, the proposed system is highly generic, and thus, applicable to any ECG dataset.
    Keywords: Signal Processing, Computer-Assisted ; Arrhythmias, Cardiac -- Physiopathology ; Electrocardiography -- Methods ; Pattern Recognition, Automated -- Methods
    ISSN: 00189294
    E-ISSN: 1558-2531
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  • 10
    In: IEEE Transactions on Multimedia, 2007, Vol.9(1), pp.102-119
    Description: One of the challenges in the development of a content-based multimedia indexing and retrieval application is to achieve an efficient indexing scheme. The developers and users who are accustomed to making queries to retrieve a particular multimedia item from a large scale database can be frustrated by the long query times. Conventional indexing structures cannot usually cope with the requirements of a multimedia database, such as dynamic indexing or the presence of high-dimensional audiovisual features. Such structures do not scale well with the ever increasing size of multimedia databases whilst inducing corruption and resulting in an over-crowded indexing structure. This paper addresses such problems and presents a novel indexing technique, hierarchical cellular tree (HCT), which is designed to bring an effective solution especially for indexing large multimedia databases. Furthermore it provides an enhanced browsing capability, which enables user to make a guided tour within the database. A pre-emptive cell-search mechanism is introduced in order to prevent corruption, which may occur due to erroneous item insertions. Among the hierarchical levels that are built in a bottom-up fashion, similar items are collected into appropriate cellular structures at some level. Cells are subject to mitosis operations when the dissimilarity exceeds a required level. By mitosis operations, cells are kept focused and compact and yet, they can grow into any dimension as long as the compactness is maintained. The proposed indexing scheme is then used along with a recently introduced query method, the progressive query, in order to achieve the ultimate goal, from the user point of view that is retrieval of the most relevant items in the earliest possible time regardless of the database size. Experimental results show that the speed of retrievals is significantly improved and the indexing structure shows no sign of degradations when the database size is increased. Furthermore, HCT ind- exing body can conveniently be used for efficient browsing and navigation operations among the multimedia database items
    Keywords: Cellular ; Databases ; Indexing ; Mitosis ; Multimedia ; Queries ; Retrieval ; Trees ; Electronics and Communications Milieux (General) (Ea) ; Multimedia Information Systems (Ci) ; (An);
    ISSN: 1520-9210
    E-ISSN: 19410077
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